use gradio

app.py

@@ -1,13 +1,12 @@
 import transformers
 import torch
 import tokenizers
-import streamlit as st
+import gradio as gr
 import re
 
 from PIL import Image
 
 
-@st.cache(hash_funcs={tokenizers.Tokenizer: lambda _: None, tokenizers.AddedToken: lambda _: None, re.Pattern: lambda _: None}, allow_output_mutation=True, suppress_st_warning=True)
 def get_model(model_name, model_path='pytorch_model.bin'):
     tokenizer = transformers.GPT2Tokenizer.from_pretrained(model_name)
     model = transformers.OPTForCausalLM.from_pretrained(model_name)
@@ -34,28 +33,18 @@ def predict(text, model, tokenizer, n_beams=5, temperature=2.5, top_p=0.8, lengt
     return list(map(tokenizer.decode, out))[0]
 
 
-model, tokenizer = get_model('facebook/opt-13b', 'OPT13b-skeptic.bin')
+model, tokenizer = get_model('big-kek/NeuroSkeptic', 'big-kek/NeuroSkeptic')
 
-# st.title("NeuroKorzh")
+example = 'Who is Bill Gates really?'
+demo = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.components.Textbox(label="what is your interest?",value = example),
+    ],
+    outputs=[
+        gr.components.Textbox(label="oh! my ...",interactive = False),
+    ],
+)
 
-# image = Image.open('korzh.jpg')
-# st.image(image, caption='НейроКорж')
+demo.launch()
 
-# option = st.selectbox('Выберите своего Коржа', ('Быстрый', 'Глубокий'))
-craziness = st.slider(label='Craziness', min_value=0, max_value=100, value=50, step=5)
-temperature = 2 + craziness / 50.
-
-st.markdown("\n")
-
-text = st.text_area(label='What are you interested in?', value='Covid - a worldwide conspiracy?', height=80)
-button = st.button('Go')
-
-if button:
-    try:
-        with st.spinner('Finding out the truth'):
-            result = predict(text, model, tokenizer, temperature=temperature)
-        
-        st.text_area(label='', value=result, height=1100)
-    
-    except Exception:
-        st.error("Ooooops, something went wrong. Please try again and report to me, tg: @vladyur")